{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5de15734",
   "metadata": {},
   "source": [
    "# DAY 18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babc3505",
   "metadata": {},
   "source": [
    "选用昨天kmeans得到的效果进行聚类，进而推断出每个簇的实际含义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dad6fdee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先运行之前预处理好的代码\n",
    "import pandas as pd\n",
    "import pandas as pd    #用于数据处理和分析，可处理表格数据。\n",
    "import numpy as np     #用于数值计算，提供了高效的数组操作。\n",
    "import matplotlib.pyplot as plt    #用于绘制各种类型的图表\n",
    "import seaborn as sns   #基于matplotlib的高级绘图库，能绘制更美观的统计图形。\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    " \n",
    " # 设置中文字体（解决中文显示问题）\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows系统常用黑体字体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 正常显示负号\n",
    "data = pd.read_csv('data.csv')    #读取数据\n",
    "\n",
    "\n",
    "# 先筛选字符串变量 \n",
    "discrete_features = data.select_dtypes(include=['object']).columns.tolist()\n",
    "# Home Ownership 标签编码\n",
    "home_ownership_mapping = {\n",
    "    'Own Home': 1,\n",
    "    'Rent': 2,\n",
    "    'Have Mortgage': 3,\n",
    "    'Home Mortgage': 4\n",
    "}\n",
    "data['Home Ownership'] = data['Home Ownership'].map(home_ownership_mapping)\n",
    "\n",
    "# Years in current job 标签编码\n",
    "years_in_job_mapping = {\n",
    "    '< 1 year': 1,\n",
    "    '1 year': 2,\n",
    "    '2 years': 3,\n",
    "    '3 years': 4,\n",
    "    '4 years': 5,\n",
    "    '5 years': 6,\n",
    "    '6 years': 7,\n",
    "    '7 years': 8,\n",
    "    '8 years': 9,\n",
    "    '9 years': 10,\n",
    "    '10+ years': 11\n",
    "}\n",
    "data['Years in current job'] = data['Years in current job'].map(years_in_job_mapping)\n",
    "\n",
    "# Purpose 独热编码，记得需要将bool类型转换为数值\n",
    "data = pd.get_dummies(data, columns=['Purpose'])\n",
    "data2 = pd.read_csv(\"data.csv\") # 重新读取数据，用来做列名对比\n",
    "list_final = [] # 新建一个空列表，用于存放独热编码后新增的特征名\n",
    "for i in data.columns:\n",
    "    if i not in data2.columns:\n",
    "       list_final.append(i) # 这里打印出来的就是独热编码后的特征名\n",
    "for i in list_final:\n",
    "    data[i] = data[i].astype(int) # 这里的i就是独热编码后的特征名\n",
    "\n",
    "\n",
    "\n",
    "# Term 0 - 1 映射\n",
    "term_mapping = {\n",
    "    'Short Term': 0,\n",
    "    'Long Term': 1\n",
    "}\n",
    "data['Term'] = data['Term'].map(term_mapping)\n",
    "data.rename(columns={'Term': 'Long Term'}, inplace=True) # 重命名列\n",
    "continuous_features = data.select_dtypes(include=['int64', 'float64']).columns.tolist()  #把筛选出来的列名转换成列表\n",
    " \n",
    " # 连续特征用中位数补全\n",
    "for feature in continuous_features:     \n",
    "    mode_value = data[feature].mode()[0]            #获取该列的众数。\n",
    "    data[feature].fillna(mode_value, inplace=True)          #用众数填充该列的缺失值，inplace=True表示直接在原数据上修改。\n",
    "\n",
    "# 最开始也说了 很多调参函数自带交叉验证，甚至是必选的参数，你如果想要不交叉反而实现起来会麻烦很多\n",
    "# 所以这里我们还是只划分一次数据集\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = data.drop(['Credit Default'], axis=1)  # 特征，axis=1表示按列删除\n",
    "y = data['Credit Default'] # 标签\n",
    "# # 按照8:2划分训练集和测试集\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # 80%训练集，20%测试集\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7a9a7e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 标准化数据（聚类前通常需要标准化）\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "# X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb09c9e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=2, 惯性: 218529.50, 轮廓系数: 0.320, CH 指数: 479.34, DB 指数: 3.222\n",
      "k=3, 惯性: 207982.87, 轮廓系数: 0.209, CH 指数: 441.88, DB 指数: 2.906\n",
      "k=4, 惯性: 200477.28, 轮廓系数: 0.220, CH 指数: 399.12, DB 指数: 2.441\n",
      "k=5, 惯性: 192940.36, 轮廓系数: 0.224, CH 指数: 384.19, DB 指数: 2.042\n",
      "k=6, 惯性: 185411.81, 轮廓系数: 0.227, CH 指数: 380.64, DB 指数: 1.733\n",
      "k=7, 惯性: 178444.49, 轮廓系数: 0.130, CH 指数: 378.31, DB 指数: 1.633\n",
      "k=8, 惯性: 174920.27, 轮廓系数: 0.143, CH 指数: 352.31, DB 指数: 1.817\n",
      "k=9, 惯性: 167383.96, 轮廓系数: 0.150, CH 指数: 364.27, DB 指数: 1.636\n",
      "k=10, 惯性: 159824.84, 轮廓系数: 0.156, CH 指数: 378.43, DB 指数: 1.502\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans Cluster labels (k=3) added to X:\n",
      "KMeans_Cluster\n",
      "0                 5205\n",
      "1                 1381\n",
      "2                  914\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 评估不同 k 值下的指标\n",
    "k_range = range(2, 11)  # 测试 k 从 2 到 10\n",
    "inertia_values = []\n",
    "silhouette_scores = []\n",
    "ch_scores = []\n",
    "db_scores = []\n",
    "\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans_labels = kmeans.fit_predict(X_scaled)\n",
    "    inertia_values.append(kmeans.inertia_)  # 惯性（肘部法则）\n",
    "    silhouette = silhouette_score(X_scaled, kmeans_labels)  # 轮廓系数\n",
    "    silhouette_scores.append(silhouette)\n",
    "    ch = calinski_harabasz_score(X_scaled, kmeans_labels)  # CH 指数\n",
    "    ch_scores.append(ch)\n",
    "    db = davies_bouldin_score(X_scaled, kmeans_labels)  # DB 指数\n",
    "    db_scores.append(db)\n",
    "    print(f\"k={k}, 惯性: {kmeans.inertia_:.2f}, 轮廓系数: {silhouette:.3f}, CH 指数: {ch:.2f}, DB 指数: {db:.3f}\")\n",
    "\n",
    "# # 绘制评估指标图\n",
    "# plt.figure(figsize=(15, 10))\n",
    "\n",
    "# # 肘部法则图（Inertia）\n",
    "# plt.subplot(2, 2, 1)\n",
    "# plt.plot(k_range, inertia_values, marker='o')\n",
    "# plt.title('肘部法则确定最优聚类数 k（惯性，越小越好）')\n",
    "# plt.xlabel('聚类数 (k)')\n",
    "# plt.ylabel('惯性')\n",
    "# plt.grid(True)\n",
    "\n",
    "# # 轮廓系数图\n",
    "# plt.subplot(2, 2, 2)\n",
    "# plt.plot(k_range, silhouette_scores, marker='o', color='orange')\n",
    "# plt.title('轮廓系数确定最优聚类数 k（越大越好）')\n",
    "# plt.xlabel('聚类数 (k)')\n",
    "# plt.ylabel('轮廓系数')\n",
    "# plt.grid(True)\n",
    "\n",
    "# # CH 指数图\n",
    "# plt.subplot(2, 2, 3)\n",
    "# plt.plot(k_range, ch_scores, marker='o', color='green')\n",
    "# plt.title('Calinski-Harabasz 指数确定最优聚类数 k（越大越好）')\n",
    "# plt.xlabel('聚类数 (k)')\n",
    "# plt.ylabel('CH 指数')\n",
    "# plt.grid(True)\n",
    "\n",
    "# # DB 指数图\n",
    "# plt.subplot(2, 2, 4)\n",
    "# plt.plot(k_range, db_scores, marker='o', color='red')\n",
    "# plt.title('Davies-Bouldin 指数确定最优聚类数 k（越小越好）')\n",
    "# plt.xlabel('聚类数 (k)')\n",
    "# plt.ylabel('DB 指数')\n",
    "# plt.grid(True)\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.show()\n",
    "\n",
    "# 提示用户选择 k 值\n",
    "selected_k = 3 # 这里选择3后面好分析，也可以根据图选择最佳的k值\n",
    "\n",
    "# 使用选择的 k 值进行 KMeans 聚类\n",
    "kmeans = KMeans(n_clusters=selected_k, random_state=42)\n",
    "kmeans_labels = kmeans.fit_predict(X_scaled)\n",
    "X['KMeans_Cluster'] = kmeans_labels\n",
    "\n",
    "# 使用 PCA 降维到 2D 进行可视化\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "# KMeans 聚类结果可视化\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=kmeans_labels, palette='viridis')\n",
    "plt.title(f'KMeans Clustering with k={selected_k} (PCA Visualization)')\n",
    "plt.xlabel('PCA Component 1')\n",
    "plt.ylabel('PCA Component 2')\n",
    "plt.show()\n",
    "\n",
    "# 打印 KMeans 聚类标签的前几行\n",
    "print(f\"KMeans Cluster labels (k={selected_k}) added to X:\")\n",
    "print(X[['KMeans_Cluster']].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d62669",
   "metadata": {},
   "source": [
    "现在需要给这个簇赋予实际的含义，一般当你赋予实际含义的时候，你需要根据某几个特征来赋予，但是源数据特征很多，如何选择特征呢？有2种思路：\n",
    "\n",
    "1. 你最开始聚类的时候，就选择了你想最后用来确定簇含义的特征，那么你需要选择一些特征来进行聚类，那么你最后确定簇含义的特征就是这几个特征，而非全部。如你想聚类消费者购买习惯，那么他过去的消费记录、购买记录、购买金额等等，这些特征都与消费者购买习惯有关，你可以使用这些特征来确定簇含义，一些其他的特征，如消费者年龄，工作行业则不考虑。----适用于你本身就有构造某些明确含义的特征的情况。\n",
    "\n",
    "2. 最开始用全部特征来聚类，把其余特征作为 x，聚类得到的簇类别作为标签构建监督模型，进而根据重要性筛选特征，来确定要根据哪些特征赋予含义。---使用于你想构造什么，目前还不清楚。\n",
    "\n",
    "***【上面这个思路好好学，这是真干货，构造具有价值的特征工程，妥妥的创新点和工作量】***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dde75089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'Home Ownership', 'Annual Income', 'Years in current job',\n",
       "       'Tax Liens', 'Number of Open Accounts', 'Years of Credit History',\n",
       "       'Maximum Open Credit', 'Number of Credit Problems',\n",
       "       'Months since last delinquent', 'Bankruptcies', 'Long Term',\n",
       "       'Current Loan Amount', 'Current Credit Balance', 'Monthly Debt',\n",
       "       'Credit Score', 'Purpose_business loan', 'Purpose_buy a car',\n",
       "       'Purpose_buy house', 'Purpose_debt consolidation',\n",
       "       'Purpose_educational expenses', 'Purpose_home improvements',\n",
       "       'Purpose_major purchase', 'Purpose_medical bills', 'Purpose_moving',\n",
       "       'Purpose_other', 'Purpose_renewable energy', 'Purpose_small business',\n",
       "       'Purpose_take a trip', 'Purpose_vacation', 'Purpose_wedding',\n",
       "       'KMeans_Cluster'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "709fd785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1= X.drop('KMeans_Cluster',axis=1) # 删除聚类标签列\n",
    "y1 = X['KMeans_Cluster']\n",
    "# 构建随机森林，用shap重要性来筛选重要性\n",
    "import shap\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier  # 随机森林分类器\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)  # 随机森林模型\n",
    "model.fit(x1, y1)  # 训练模型,此时无需在意准确率 直接全部数据用来训练了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "38ec28b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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e=null!==i?i.baseLanes|n:n,t.lanes=t.childLanes=1073741824,t.memoizedState={baseLanes:e,cachePool:null,transitions:null},t.updateQueue=null,Ca(Al,Ol),Ol|=e,null;t.memoizedState={baseLanes:0,cachePool:null,transitions:null},r=null!==i?i.baseLanes:n,Ca(Al,Ol),Ol|=r}else null!==i?(r=i.baseLanes|n,t.memoizedState=null):r=n,Ca(Al,Ol),Ol|=r;return wu(e,t,a,n),t.child}function Cu(e,t){var n=t.ref;(null===e&&null!==n||null!==e&&e.ref!==n)&&(t.flags|=512,t.flags|=2097152)}function Tu(e,t,n,r,a){var i=La(n)?Pa:Ma.current;return i=za(t,i),Si(t,a),n=So(e,t,n,r,i,a),r=Eo(),null===e||_u?(ai&&r&&ei(t),t.flags|=1,wu(e,t,n,a),t.child):(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~a,Hu(e,t,a))}function Mu(e,t,n,r,a){if(La(n)){var i=!0;Da(t)}else i=!1;if(Si(t,a),null===t.stateNode)Vu(e,t),Wi(t,n,r),Hi(t,n,r,a),r=!0;else if(null===e){var o=t.stateNode,u=t.memoizedProps;o.props=u;var l=o.context,s=n.contextType;s=\"object\"==typeof s&&null!==s?Ei(s):za(t,s=La(n)?Pa:Ma.current);var c=n.getDerivedStateFromProps,f=\"function\"==typeof c||\"function\"==typeof o.getSnapshotBeforeUpdate;f||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==r||l!==s)&&Vi(t,o,r,s),Pi=!1;var p=t.memoizedState;o.state=p,Ri(t,r,o,a),l=t.memoizedState,u!==r||p!==l||Na.current||Pi?(\"function\"==typeof c&&(Ii(t,n,c,r),l=t.memoizedState),(u=Pi||Bi(t,n,u,r,p,l,s))?(f||\"function\"!=typeof o.UNSAFE_componentWillMount&&\"function\"!=typeof o.componentWillMount||(\"function\"==typeof o.componentWillMount&&o.componentWillMount(),\"function\"==typeof o.UNSAFE_componentWillMount&&o.UNSAFE_componentWillMount()),\"function\"==typeof o.componentDidMount&&(t.flags|=4194308)):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=l),o.props=r,o.state=l,o.context=s,r=u):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),r=!1)}else{o=t.stateNode,Li(e,t),u=t.memoizedProps,s=t.type===t.elementType?u:gi(t.type,u),o.props=s,f=t.pendingProps,p=o.context,l=\"object\"==typeof(l=n.contextType)&&null!==l?Ei(l):za(t,l=La(n)?Pa:Ma.current);var d=n.getDerivedStateFromProps;(c=\"function\"==typeof d||\"function\"==typeof o.getSnapshotBeforeUpdate)||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==f||p!==l)&&Vi(t,o,r,l),Pi=!1,p=t.memoizedState,o.state=p,Ri(t,r,o,a);var h=t.memoizedState;u!==f||p!==h||Na.current||Pi?(\"function\"==typeof d&&(Ii(t,n,d,r),h=t.memoizedState),(s=Pi||Bi(t,n,s,r,p,h,l)||!1)?(c||\"function\"!=typeof o.UNSAFE_componentWillUpdate&&\"function\"!=typeof o.componentWillUpdate||(\"function\"==typeof o.componentWillUpdate&&o.componentWillUpdate(r,h,l),\"function\"==typeof o.UNSAFE_componentWillUpdate&&o.UNSAFE_componentWillUpdate(r,h,l)),\"function\"==typeof o.componentDidUpdate&&(t.flags|=4),\"function\"==typeof o.getSnapshotBeforeUpdate&&(t.flags|=1024)):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),o.props=r,o.state=h,o.context=l,r=s):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),r=!1)}return Nu(e,t,n,r,i,a)}function Nu(e,t,n,r,a,i){Cu(e,t);var o=0!=(128&t.flags);if(!r&&!o)return a&&Ra(t,n,!1),Hu(e,t,i);r=t.stateNode,bu.current=t;var u=o&&\"function\"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&o?(t.child=Ki(t,e.child,null,i),t.child=Ki(t,null,u,i)):wu(e,t,u,i),t.memoizedState=r.state,a&&Ra(t,n,!0),t.child}function Pu(e){var t=e.stateNode;t.pendingContext?Aa(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Aa(0,t.context,!1),ro(e,t.containerInfo)}function zu(e,t,n,r,a){return di(),hi(a),t.flags|=256,wu(e,t,n,r),t.child}var Lu,Ou,Au,Fu,Du={dehydrated:null,treeContext:null,retryLane:0};function Ru(e){return{baseLanes:e,cachePool:null,transitions:null}}function ju(e,t,n){var r,a=t.pendingProps,o=uo.current,u=!1,l=0!=(128&t.flags);if((r=l)||(r=(null===e||null!==e.memoizedState)&&0!=(2&o)),r?(u=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(o|=1),Ca(uo,1&o),null===e)return si(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0==(1&t.mode)?t.lanes=1:\"$!\"===e.data?t.lanes=8:t.lanes=1073741824,null):(l=a.children,e=a.fallback,u?(a=t.mode,u=t.child,l={mode:\"hidden\",children:l},0==(1&a)&&null!==u?(u.childLanes=0,u.pendingProps=l):u=Rs(l,a,0,null),e=Ds(e,a,n,null),u.return=t,e.return=t,u.sibling=e,t.child=u,t.child.memoizedState=Ru(n),t.memoizedState=Du,e):Uu(t,l));if(null!==(o=e.memoizedState)&&null!==(r=o.dehydrated))return function(e,t,n,r,a,o,u){if(n)return 256&t.flags?(t.flags&=-257,Iu(e,t,u,r=fu(Error(i(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(o=r.fallback,a=t.mode,r=Rs({mode:\"visible\",children:r.children},a,0,null),(o=Ds(o,a,u,null)).flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,0!=(1&t.mode)&&Ki(t,e.child,null,u),t.child.memoizedState=Ru(u),t.memoizedState=Du,o);if(0==(1&t.mode))return Iu(e,t,u,null);if(\"$!\"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var l=r.dgst;return r=l,Iu(e,t,u,r=fu(o=Error(i(419)),r,void 0))}if(l=0!=(u&e.childLanes),_u||l){if(null!==(r=Pl)){switch(u&-u){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 536870912:a=268435456;break;default:a=0}0!==(a=0!=(a&(r.suspendedLanes|u))?0:a)&&a!==o.retryLane&&(o.retryLane=a,Ni(e,a),rs(r,e,a,-1))}return gs(),Iu(e,t,u,r=fu(Error(i(421))))}return\"$?\"===a.data?(t.flags|=128,t.child=e.child,t=Ms.bind(null,e),a._reactRetry=t,null):(e=o.treeContext,ri=sa(a.nextSibling),ni=t,ai=!0,ii=null,null!==e&&(Qa[Ya++]=Ka,Qa[Ya++]=Za,Qa[Ya++]=Ga,Ka=e.id,Za=e.overflow,Ga=t),(t=Uu(t,r.children)).flags|=4096,t)}(e,t,l,a,r,o,n);if(u){u=a.fallback,l=t.mode,r=(o=e.child).sibling;var s={mode:\"hidden\",children:a.children};return 0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var i=e.memoizedState;null===i?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function Wu(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(wu(e,t,r.children,n),0!=(2&(r=uo.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!=(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&$u(e,n,t);else if(19===e.tag)$u(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Ca(uo,r),0==(1&t.mode))t.memoizedState=null;else switch(a){case\"forwards\":for(n=t.child,a=null;null!==n;)null!==(e=n.alternate)&&null===lo(e)&&(a=n),n=n.sibling;null===(n=a)?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),Bu(t,!1,a,n,i);break;case\"backwards\":for(n=null,a=t.child,t.child=null;null!==a;){if(null!==(e=a.alternate)&&null===lo(e)){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}Bu(t,!0,n,null,i);break;case\"together\":Bu(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Vu(e,t){0==(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Hu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),Rl|=t.lanes,0==(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(i(153));if(null!==t.child){for(n=As(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=As(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function qu(e,t){if(!ai)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Qu(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=14680064&a.subtreeFlags,r|=14680064&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Yu(e,t,n){var r=t.pendingProps;switch(ti(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Qu(t),null;case 1:case 17:return La(t.type)&&Oa(),Qu(t),null;case 3:return r=t.stateNode,ao(),Ea(Na),Ea(Ma),co(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),null!==e&&null!==e.child||(fi(t)?t.flags|=4:null===e||e.memoizedState.isDehydrated&&0==(256&t.flags)||(t.flags|=1024,null!==ii&&(us(ii),ii=null))),Ou(e,t),Qu(t),null;case 5:oo(t);var a=no(to.current);if(n=t.type,null!==e&&null!=t.stateNode)Au(e,t,n,r,a),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(null===t.stateNode)throw Error(i(166));return Qu(t),null}if(e=no(Ji.current),fi(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[pa]=t,r[da]=o,e=0!=(1&t.mode),n){case\"dialog\":Ur(\"cancel\",r),Ur(\"close\",r);break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",r);break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],r);break;case\"source\":Ur(\"error\",r);break;case\"img\":case\"image\":case\"link\":Ur(\"error\",r),Ur(\"load\",r);break;case\"details\":Ur(\"toggle\",r);break;case\"input\":K(r,o),Ur(\"invalid\",r);break;case\"select\":r._wrapperState={wasMultiple:!!o.multiple},Ur(\"invalid\",r);break;case\"textarea\":ae(r,o),Ur(\"invalid\",r)}for(var l in me(n,o),a=null,o)if(o.hasOwnProperty(l)){var s=o[l];\"children\"===l?\"string\"==typeof s?r.textContent!==s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",s]):\"number\"==typeof s&&r.textContent!==\"\"+s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",\"\"+s]):u.hasOwnProperty(l)&&null!=s&&\"onScroll\"===l&&Ur(\"scroll\",r)}switch(n){case\"input\":q(r),J(r,o,!0);break;case\"textarea\":q(r),oe(r);break;case\"select\":case\"option\":break;default:\"function\"==typeof o.onClick&&(r.onclick=Jr)}r=a,t.updateQueue=r,null!==r&&(t.flags|=4)}else{l=9===a.nodeType?a:a.ownerDocument,\"http://www.w3.org/1999/xhtml\"===e&&(e=ue(n)),\"http://www.w3.org/1999/xhtml\"===e?\"script\"===n?((e=l.createElement(\"div\")).innerHTML=\"<script><\\/script>\",e=e.removeChild(e.firstChild)):\"string\"==typeof r.is?e=l.createElement(n,{is:r.is}):(e=l.createElement(n),\"select\"===n&&(l=e,r.multiple?l.multiple=!0:r.size&&(l.size=r.size))):e=l.createElementNS(e,n),e[pa]=t,e[da]=r,Lu(e,t,!1,!1),t.stateNode=e;e:{switch(l=be(n,r),n){case\"dialog\":Ur(\"cancel\",e),Ur(\"close\",e),a=r;break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",e),a=r;break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],e);a=r;break;case\"source\":Ur(\"error\",e),a=r;break;case\"img\":case\"image\":case\"link\":Ur(\"error\",e),Ur(\"load\",e),a=r;break;case\"details\":Ur(\"toggle\",e),a=r;break;case\"input\":K(e,r),a=G(e,r),Ur(\"invalid\",e);break;case\"option\":default:a=r;break;case\"select\":e._wrapperState={wasMultiple:!!r.multiple},a=R({},r,{value:void 0}),Ur(\"invalid\",e);break;case\"textarea\":ae(e,r),a=re(e,r),Ur(\"invalid\",e)}for(o in me(n,a),s=a)if(s.hasOwnProperty(o)){var c=s[o];\"style\"===o?ge(e,c):\"dangerouslySetInnerHTML\"===o?null!=(c=c?c.__html:void 0)&&fe(e,c):\"children\"===o?\"string\"==typeof c?(\"textarea\"!==n||\"\"!==c)&&pe(e,c):\"number\"==typeof c&&pe(e,\"\"+c):\"suppressContentEditableWarning\"!==o&&\"suppressHydrationWarning\"!==o&&\"autoFocus\"!==o&&(u.hasOwnProperty(o)?null!=c&&\"onScroll\"===o&&Ur(\"scroll\",e):null!=c&&b(e,o,c,l))}switch(n){case\"input\":q(e),J(e,r,!1);break;case\"textarea\":q(e),oe(e);break;case\"option\":null!=r.value&&e.setAttribute(\"value\",\"\"+V(r.value));break;case\"select\":e.multiple=!!r.multiple,null!=(o=r.value)?ne(e,!!r.multiple,o,!1):null!=r.defaultValue&&ne(e,!!r.multiple,r.defaultValue,!0);break;default:\"function\"==typeof a.onClick&&(e.onclick=Jr)}switch(n){case\"button\":case\"input\":case\"select\":case\"textarea\":r=!!r.autoFocus;break e;case\"img\":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}null!==t.ref&&(t.flags|=512,t.flags|=2097152)}return Qu(t),null;case 6:if(e&&null!=t.stateNode)Fu(e,t,e.memoizedProps,r);else{if(\"string\"!=typeof r&&null===t.stateNode)throw 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0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return 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S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var 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.force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 300px;\\n          }\\n          .additive-force-array-xlabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-ylabel {\\n            position: relative;\\n            top: 0px;\\n            left: 0px;\\n            transform: rotate(-90deg);\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            max-width: 150px;\\n          }\\n          .additive-force-array-ylabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-hoverLine {\\n            stroke-width: 1px;\\n            stroke: #fff;\\n            opacity: 1;\\n          }\"}}),e.createElement(\"select\",{className:\"additive-force-array-xlabel\"}),e.createElement(\"div\",{style:{height:\"0px\",textAlign:\"left\"}},e.createElement(\"select\",{className:\"additive-force-array-ylabel\"})),e.createElement(\"svg\",{ref:function(e){return t.svg=Jt(e)},style:{userSelect:\"none\",display:\"block\",fontFamily:\"arial\",sansSerif:!0}}))}}])&&Xa(n.prototype,r),Object.defineProperty(n,\"prototype\",{writable:!1}),u}(e.Component);ni.defaultProps={plot_cmap:\"RdBu\",ordering_keys:null,ordering_keys_time_format:null};const ri=ni;window.SHAP={SimpleListVisualizer:He,AdditiveForceVisualizer:Ln,AdditiveForceArrayVisualizer:ri,React:e,ReactDom:t}})()})();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(7500, 31, 3)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.initjs()\n",
    "# 初始化 SHAP 解释器\n",
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values = explainer.shap_values(x1) # 这个计算耗时\n",
    "shap_values.shape # 第一维是样本数，第二维是特征数，第三维是类别数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bb5d7da4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 1. SHAP 特征重要性条形图 ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- 1. SHAP 特征重要性条形图 (Summary Plot - Bar) ---\n",
    "print(\"--- 1. SHAP 特征重要性条形图 ---\")\n",
    "shap.summary_plot(shap_values[:, :, 0], x1, plot_type=\"bar\",show=False)  #  这里的show=False表示不直接显示图形,这样可以继续用plt来修改元素，不然就直接输出了\n",
    "plt.title(\"SHAP Feature Importance (Bar Plot)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a61028e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purpose_debt consolidation 的唯一值数量: 2\n",
      "Purpose_debt consolidation 可能是离散型变量\n",
      "Bankruptcies 的唯一值数量: 5\n",
      "Bankruptcies 可能是离散型变量\n",
      "Number of Credit Problems 的唯一值数量: 8\n",
      "Number of Credit Problems 可能是离散型变量\n",
      "Purpose_other 的唯一值数量: 2\n",
      "Purpose_other 可能是离散型变量\n"
     ]
    }
   ],
   "source": [
    "# 此时判断一下这几个特征是离散型还是连续型\n",
    "import pandas as pd\n",
    "selected_features = ['Purpose_debt consolidation', 'Bankruptcies',\n",
    "                     'Number of Credit Problems', 'Purpose_other']\n",
    "\n",
    "for feature in selected_features:\n",
    "    unique_count = X[feature].nunique() # 唯一值指的是在某一列或某个特征中，不重复出现的值\n",
    "    # 连续型变量通常有很多唯一值，而离散型变量的唯一值较少\n",
    "    print(f'{feature} 的唯一值数量: {unique_count}')\n",
    "    if unique_count < 10:  # 这里 10 是一个经验阈值，可以根据实际情况调整\n",
    "        print(f'{feature} 可能是离散型变量')\n",
    "    else:\n",
    "        print(f'{feature} 可能是连续型变量')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8aa3c203",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X[\"Purpose_debt consolidation\"].value_counts() # 统计每个唯一值的出现次数\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 总样本中的前四个重要性的特征分布图\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, feature in enumerate(selected_features):\n",
    "    axes[i].hist(X[feature], bins=20)\n",
    "    axes[i].set_title(f'Histogram of {feature}')\n",
    "    axes[i].set_xlabel(feature)\n",
    "    axes[i].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ad6fa679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans_Cluster\n",
       "0                 5205\n",
       "1                 1381\n",
       "2                  914\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制出每个簇对应的这四个特征的分布图\n",
    "X[['KMeans_Cluster']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d5d749f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分别筛选出每个簇的数据\n",
    "X_cluster0 = X[X['KMeans_Cluster'] == 0]\n",
    "X_cluster1 = X[X['KMeans_Cluster'] == 1]\n",
    "X_cluster2 = X[X['KMeans_Cluster'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b60ba7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 先绘制簇0的分布图\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 总样本中的前四个重要性的特征分布图\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, feature in enumerate(selected_features):\n",
    "    axes[i].hist(X_cluster0[feature], bins=20)\n",
    "    axes[i].set_title(f'Histogram of {feature}')\n",
    "    axes[i].set_xlabel(feature)\n",
    "    axes[i].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "414ee75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 先绘制簇0的分布图\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 总样本中的前四个重要性的特征分布图\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, feature in enumerate(selected_features):\n",
    "    axes[i].hist(X_cluster1[feature], bins=20)\n",
    "    axes[i].set_title(f'Histogram of {feature}')\n",
    "    axes[i].set_xlabel(feature)\n",
    "    axes[i].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "82a50fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 先绘制簇2的分布图\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 总样本中的前四个重要性的特征分布图\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, feature in enumerate(selected_features):\n",
    "    axes[i].hist(X_cluster2[feature], bins=20)\n",
    "    axes[i].set_title(f'Histogram of {feature}')\n",
    "    axes[i].set_xlabel(feature)\n",
    "    axes[i].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bce3739",
   "metadata": {},
   "source": [
    "把这三个簇的图发给ai，让ai给你定义----身在好时代了啊你们！\n",
    "\n",
    "### 三个簇的总结与定义\n",
    "1. **第一个簇 - 优质信用稳健财务型**\n",
    "    - **特征总结**：在债务合并用途上表现一致，几乎无破产记录，信用问题极少，资金用途集中且很少涉及特殊类别。财务状况稳定，信用良好，资金流向明确。\n",
    "    - **定义依据**：各项关键财务和信用指标表现优异，显示出良好的财务自律性和信用履约能力，所以定义为 “优质信用稳健财务型”。\n",
    "2. **第二个簇 - 较稳健但信用有分化财务型**\n",
    "    - **特征总结**：多数无债务合并需求，破产情况少见，但信用问题上存在个体差异，资金用途有一定分散性。整体财务状况相对稳定，但在信用和资金使用方向上不如第一个簇表现一致。\n",
    "    - **定义依据**：虽然总体财务状况尚可，但信用记录和资金用途的分化情况使其区别于第一个簇，因此定义为 “较稳健但信用有分化财务型”。 \n",
    "3. **第三个簇 - 高风险财务困境型**\n",
    "    - **特征总结**：债务合并需求分化，破产经历较多，信用问题普遍且严重，资金用途有差异。在财务健康和信用方面存在诸多问题，面临较大财务风险。\n",
    "    - **定义依据**：基于明显的财务困境迹象和高风险特征，定义为 “高风险财务困境型”。 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d89f19e2",
   "metadata": {},
   "source": [
    "现在你就得到了一个全新的特征，完成了你的特征工程\n",
    "\n",
    "后续研究需要对这个特征独热编码，然后重新建模训练，如果加了这个特征后模型精度提高，说明这个特征是有用的。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DL",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
